In manufacturing of varied products, such as manufacturing of metal dies, an indefinite time duration is consumed in shape modelling, NC processing, machining or a preparatory process, for machining metal die parts requiring intricate shaping from step to step. As a result it is almost impossible to calculate accurately the process time and hence to set up accurate scheduling through to the end of manufacturing. The present invention overcomes this drawback. More specifically, the present invention relates to an apparatus for estimating the time of the manufacturing processes whereby the process time may be estimated with high accuracy by means of a neural network having the self-learning and self-multiplicative functions. By taking account of the fact that the factors of machining and machining time are correlated with each other, it is possible with the neural network to make accurate estimation of the process time, by relying upon its learning function by weighting the coupling coefficients between different neurons with indefinite factors involved in shape modelling, NC processing, machining or preparatory process by taking account of workshop or operating environments. Thus the present process time estimation apparatus renders it possible to make highly accurate estimation of the process time in the production of varied products to set up an accurate production schedule through the completion of manufacturing.